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To investigate environmentally friendly alternatives for sludge disposal, three proportions of secondary sludge (SS) from two pulping processes (Kraft and TMP) were incorporated in the formulation of particleboard manufacturing. A 3² factorial design was used where the factors were Urea-formaldehyde (UF) content (5%, 7%, and 9% dry weight of resin per dry weight of particles) and secondary sludge percentage (75%, 100%, and 125% dry weight of SS per dry weight of resin). For each pulping process, 27 panels with SS and 3 control panels (without SS for each resin content) were made for a total of 63 panels. All panels were tested for thickness swell, linear expansion, internal bond strength (IB), flexural modulus of elasticity (MOE) and flexural modulus of rupture (MOR). Results indicated that particleboards made with SS from both pulping processes met the ANSI standards for linear expansion, IB, MOE and MOR. However, none of the tested panels met the standard for thickness swell and adding SS to the formulation affected negatively this property. It was concluded that SS from TMP and Kraft mills can be used to manufacture particleboard panels. However, its' percentage along with other additives' content should be optimized.

The purpose of this paper is to test for gender-specific effects on odor-induced taste enhancement and subsequent food consumption in olfactory food marketing.

Lab experiments conducted among female and male participants using vanillin as a stimulus and ratings of sweetness, taste pleasantness and eating of sugar-free food as measures.

Odor-induced taste enhancement is gender-specific. Female consumers outperform male consumers in olfactory reaction and sweetness perception. While men outperform women in food consumption.

Odor intensity was set to the concentration level of 0.00005per cent according to the findings from (Fujimaru and Lim, 2013). The authors believe that this intensity level is appropriate for both men and women. Still, there may be some gender effects on intensity levels, which are not explored here. The author’s test for the effects of one personal factor, gender and odor-induced taste enhancement of sugar-free food. The authors think that investigating the combined effects of more personal factors such as age, culture and so on adds to the accuracy of the results.

It seems that the stronger sensory capacities of women in terms of odor detection and recognition already confirmed in the literature extends to the cross-modal effects of this sensory detection and recognition on taste enhancement. It seems appropriate to tailor olfactory food advertising according to the gender of the target audience.

Odor-induced taste enhancement is still a novel subject in marketing. While most of the research has investigated the effects of smelling congruent odors on taste perception and food consumption among mixed groups of men and women, the value of this paper lies in the investigation of the potential moderating effects of gender on this relationship.

ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.

Classifying emails as ham or spam based on their content is essential. Determining the semantic and syntactic meaning of words and putting them in a high-dimensional feature vector form for processing is the most difficult challenge in email categorization. The purpose of this paper is to examine the effectiveness of the pre-trained embedding model for the classification of emails using deep learning classifiers such as the long short-term memory (LSTM) model and convolutional neural network (CNN) model.

In this paper, global vectors (GloVe) and Bidirectional Encoder Representations Transformers (BERT) pre-trained word embedding are used to identify relationships between words, which helps to classify emails into their relevant categories using machine learning and deep learning models. Two benchmark datasets, SpamAssassin and Enron, are used in the experimentation.

In the first set of experiments, machine learning classifiers, the support vector machine (SVM) model, perform better than other machine learning methodologies. The second set of experiments compares the deep learning model performance without embedding, GloVe and BERT embedding. The experiments show that GloVe embedding can be helpful for faster execution with better performance on large-sized datasets.

The experiment reveals that the CNN model with GloVe embedding gives slightly better accuracy than the model with BERT embedding and traditional machine learning algorithms to classify an email as ham or spam. It is concluded that the word embedding models improve email classifiers accuracy.

Citations have been used as a common basis to measure the academic accomplishments of scientific books. However, traditional citation analysis ignored content mining and without consideration of citation equivalence, which may lead to the decline of evaluation reliability. Hence, this paper aims to integrate multi-level citation information to conduct multi-dimensional analysis.

In this paper, books’ academic impacts were measured by integrating multi-level citation resources, including books’ citation frequencies and citation-related contents. Specifically, firstly, books’ citation frequencies were counted as the frequency-level metric. Secondly, content-level metrics were detected from multi-dimensional citation contents based on finer-grained mining, including topic extraction on the metadata and citation classification on the citation contexts. Finally, differential metric weighting methods were compared with integrate the multi-level metrics and computing books’ academic impacts.

The experimental results indicate that the integration of multiple citation resources is necessary, as it can significantly improve the comprehensiveness of the evaluation results. Meanwhile, compared with the type differences of books, disciplinary differences need more attention when evaluating the academic impacts of books.

Academic impact assessment of books via integrating multi-level citation information can provide more detailed evaluation information and cover shortcomings of methods based on single citation data. Moreover, the method proposed in this paper is publication independent, which can be used to measure other publications besides books.

The purpose of this paper is to highlight the strategic role of purchasing and model its transformation process based on a case study of a military firm.

The paper reviews the existing literature to highlight the strategic role of purchasing and put forward the transformations the function is undertaking. The model system dynamic approach is then detailed and applied to model the transformation of the purchasing function of a military firm. The modeling software Analytica is then used to run the model and get to results.

The shift of purchasing toward a more strategic function is complex and multidimensional. Implementing these transformations requires flexible designing approaches such as the system dynamic modeling that incorporates both quantitative and qualitative variables to model the function’s changes. The paper details the methodology of applying the system dynamic approach. It recommends a new structure of the purchasing function as well as new and upgraded indicators of purchasing performance and suppliers’ management.

A single-case study research. Even though, the objective is not to generalize the findings but to enrich the existing literature as regard the system dynamic modeling in a specific domain, the one-case research setting can be seen as a limitation against generalizeable findings.

A clear step-by-step action plan of conducting the transformation of the purchasing function using the system dynamic modeling approach. The paper gives ways to upgrade existing measures of purchasing and policies of suppliers’ management.

The application of the system dynamic modeling approach to the specific domain of military purchasing.

The objective of this article is to investigate the moderating role of national cultures in the relationship between brand value and firm value.

This article examines the topic in the context of different national cultural attributes, including individualism, uncertainty avoidance, masculinity, power distance, and long-term orientation. We use brand values of the Financial Times Global 500 companies and national cultural values reported by Hofstede, GLOBE, and Schwartz.

Results exhibit that brands are more value-additive to companies in highly individualistic cultures. Furthermore, a valuable brand contributes more to firm value in countries with low uncertainty avoidance, high masculine, low power distance, and short-term oriented cultures.

The evidence suggests that while a valuable brand contributes to firm value, the level of its effect on firm value varies by national cultures.

This research examines the complex relationship between usage of Chat Generative Pre-Trained Transformer (ChatGPT) amongst student and their creativity, learning and assessment using empirical data collected from postgraduate students. In addition, the study explores the student’s intrinsic motivation for usage to understand student categories. This research seeks to provide further insights into this artificial intelligence tool in enhancing the educational ecosystem for all stakeholders concerned.

The target population of this research – the students of post-graduation in diverse fields of science and management. A five-point Likert scale-structured questionnaire adapted from earlier literature relevant to the research questions was adopted for data collection. The data were collected for two months, resulted in 403 usable responses. Ethical considerations of assurance of confidentiality to the participants were strictly adhered to. Structured equation modelling (SEM) was employed to explore the relationships between the constructs of the study for the assessment of latent relationships. SmartPLS 4 was used to explore these relationships.

Usage has a negative impact on a student’s creativity, but increased usage of ChatGPT encourages a student’s adoption due to its perceived usability. Pedagogical applications of ChatGPT aid students as a learning tool but require controlled usage under supervision.

This study is innovative in the context of postgraduate students, where very little evidence of creativity exists. Through this research, the authors illuminate how ChatGPT use affects academic performance, benefiting educators as a tool but for evaluation and assessment, policymakers and students. The findings of the study provide implications that help to create effective digital education strategies for stakeholders.

This study aims to identify blockchain-related innovation trends that can improve trust networks in a smart city's transport and supply chain networks. Trust networks are crucial in building and maintaining the trust of citizens in smart cities. By promoting transparency and accountability, facilitating collaboration and innovation, enhancing citizen participation and protecting privacy and security, trust networks can help to ensure that smart cities are developed and implemented in a responsible and sustainable way. A systematic literature review identifies 60 conceptual and empirical studies. This research focuses on the current problems and developing procurement and supply chain strategy and the potential benefits of using blockchain in these areas. It suggests ways for the smart city's transport and supply chain networks to utilise blockchain to improve operations and supply chain strategy and identifies innovation trends related to blockchain. The study also includes a systematic literature review and Blockchain Transformation and Influence model as the basis to enhance trust networks in the supply chain.

This qualitative work aims to explore the university students’ attitude toward advantages, drawbacks and prospects of ChatGPT.

This paper applies well accepted Colaizzi’s phenomenological descriptive method of enquiry and content analysis method to reveal the ChatGPT user experience of students in the higher education level.

The study’s findings indicate that ChatGPT enhances the quality of learning and facilitates faster learning among university students. However, despite numerous positive outcomes, it is noted that ChatGPT may diminish students' creativity by swiftly addressing their critical queries. Over time, students may experience a decline in patience and critical thinking skills as they excessively rely on ChatGPT, potentially leading to ethical misconduct.

This paper primarily explores the advantages and drawbacks of using ChatGPT in the university context of Bangladesh. The present study creates a platform for future research in this domain with comprehensive study design. The study results alert the policy makers to improve upcoming version of ChatGPT with convenient user experience and academicians as this paper unleash several positive as well as negative consequences of using this AI-enabled chatbot.